Efficient Ground Truth Extraction for Federated Object Detection in Vehicular Networks

Supervisor: Ahmad Khalil
KOM-ID: KOM-M-0774 Student: Hani Aldebes
Link zur Ausschreibung

In recent years, there has been an increasing interest in adopting federated learning to train deep learning models. 
This heightened interest is primarily driven by the agility, data privacy, scalability, and efficiency that federated learning has demonstrated.
Federated learning (FL) has gained significant traction, particularly in developing and updating object detection models.
These models demand continuous refinement, a task that FL can efficiently accomplish. Nonetheless, a critical challenge for these models revolves around the collection and preparation of training data.
This issue is particularly pronounced in vehicular networks, where each vehicle or node must gather images and prepare them for their respective local models.
However, a pivotal prerequisite is labeling these images before they can be utilized in training supervised models.
This thesis presents several methodologies for image labeling, offering an in-depth exploration of each approach, and showing their respective advantages and drawbacks (e.g., in the model performance, required computational power, and network load).
Furthermore, it conducts a comprehensive assessment of the performance and accuracy of these approaches, utilizing a variety of metrics for evaluation.


  • Explore federated learning applications, with a particular focus on their utilization within the domain of vehicular applications. 
  • Extend the implementation of the pre-existing framework for training federated learning-based object detection models. This extension should encompass the integration of diverse data labeling methodologies.
  • Conduct a thorough and exhaustive evaluation of the performance of these aforementioned approaches. This assessment should encompass the utilization of a diverse array of metrics for evaluation purposes, ensuring a comprehensive understanding of their respective strengths and weaknesses.
  • Report the findings


It is required to have:
•    Motivated and individual working style
•    Very good programming skills in Python.
•    Very good machine learning / Deep learning knowledge


I am looking for motivated students interested in working on cutting-edge technologies and vehicular applications area. If you are interested in writing a Bachelor's or Master's Thesis, please feel free to contact me at Ahmad Khalil (ahmad.khalil(at)kom.tu-darmstadt.de). You can email me the following information:
•    Your CV or small text about your courses and prior experiences in this area.